Search

Objectives: Structured reporting enhances comparability, readability, and content detail. Large language models (LLMs) could convert free text into structured data without disrupting radiologists' reporting workflow. This study evaluated an on-premise, privacy-preserving LLM for automatically structuring free-text radiology reports., Materials and Methods: We developed an approach to controlling the LLM output, ensuring the validity and completeness of structured reports produced by a locally hosted Llama-2-70B-chat model. A dataset with de-identified narrative chest radiograph (CXR) reports was compiled retrospectively. It included 202 English reports from a publicly available MIMIC-CXR dataset and 197 German reports from our university hospital. Senior radiologist prepared a detailed, fully structured reporting template with 48 question-answer pairs. All reports were independently structured by the LLM and two human readers. Bayesian inference (Markov chain Monte Carlo sampling) was used to estimate the distributions of Matthews correlation coefficient (MCC), with [-0.05, 0.05] as the region of practical equivalence (ROPE)., Results: The LLM generated valid structured reports in all cases, achieving an average MCC of 0.75 (94% HDI: 0.70-0.80) and F1 score of 0.70 (0.70-0.80) for English, and 0.66 (0.62-0.70) and 0.68 (0.64-0.72) for German reports, respectively. The MCC differences between LLM and humans were within ROPE for both languages: 0.01 (-0.05 to 0.07), 0.01 (-0.05 to 0.07) for English, and -0.01 (-0.07 to 0.05), 0.00 (-0.06 to 0.06) for German, indicating approximately comparable performance., Conclusion: Locally hosted, open-source LLMs can automatically structure free-text radiology reports with approximately human accuracy. However, the understanding of semantics varied across languages and imaging findings., Key Points: Question Why has structured reporting not been widely adopted in radiology despite clear benefits and how can we improve this? Findings A locally hosted large language model successfully structured narrative reports, showing variation between languages and findings. Critical relevance Structured reporting provides many benefits, but its integration into the clinical routine is limited. Automating the extraction of structured information from radiology reports enables the capture of structured data while allowing the radiologist to maintain their reporting workflow., (© 2024. The Author(s).)

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge., (© 2024. Springer Nature Limited.)

Antibody-drug conjugates (ADCs) directed to trophoblast cell surface antigen 2 (TROP2) have gained approval as a therapeutic option for advanced triple-negative breast cancer, and TROP2 expression has been linked to unfavourable outcomes in various malignancies. In colorectal carcinoma (CRC), there is still a lack of comprehensive studies on its expression frequency and its prognostic implications in relation to the main clinicopathological parameters. We examined the expression of TROP2 in a large cohort of 1,052 CRC cases and correlated our findings with histopathological and molecular parameters, tumour stage, and patient outcomes. TROP2 was heterogeneously expressed in 214/1,052 CRCs (20.3%), with only a fraction of strongly positive tumours. TROP2 expression significantly correlated with an invasive histological phenotype (e.g. increased tumour budding/aggressive histopathological subtypes), advanced tumour stage, microsatellite stable tumours, and p53 alterations. While TROP2 expression was prognostic in univariable analyses of the overall cohort (e.g. for disease-free survival, p < 0.001), it exhibited distinct variations among important clinicopathological subgroups (e.g. right- versus left-sided CRC, microsatellite stable versus unstable CRC, Union for International Cancer Control [UICC] stages) and lost its significance in multivariable analyses that included stage and CRC histopathology. In summary, TROP2 is quite frequently expressed in CRC and associated with an aggressive histopathological phenotype and microsatellite stable tumours. Future clinical trials investigating anti-TROP2 ADCs should acknowledge the observed intratumoural heterogeneity, given that only a subset of TROP2-expressing CRC show strong positivity. The prognostic implications of TROP2 are complex and show substantial variations across crucial clinicopathological subgroups, thus indicating that TROP2 is a suboptimal parameter to predict patient prognosis., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

As neuroendocrine tumors (NETs) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathologic assessment. Because a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 neuroendocrine carcinomas (NEC) according to the immunoreactive score (IRS) and correlated PITX2 expression groups with general tumor groups and primary localization. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), whereas CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of neuroendocrine neoplasms. Therefore, our data argue toward implementation into diagnostic panels applied for NET as a firstline midgut marker., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with protein-based subtypes. However, if and how these findings extend to UTUC remains unclear. Artificial intelligence-based approaches could help elucidate UTUC's biology and extend access to targeted treatments to a wider patient audience. Here, UTUC protein-based subtypes were identified, and a deep-learning (DL) workflow was developed to predict them directly from routine histopathological H&E slides. Protein-based subtypes in a retrospective cohort of 163 invasive tumors were assigned by hierarchical clustering of the immunohistochemical expression of three luminal (FOXA1, GATA3, and CK20) and three basal (CD44, CK5, and CK14) markers. Cluster analysis identified distinctive luminal (N = 80) and basal (N = 42) subtypes. The luminal subtype mostly included pushing, papillary tumors, whereas the basal subtype diffusely infiltrating, non-papillary tumors. DL model building relied on a transfer-learning approach by fine-tuning a pre-trained ResNet50. Classification performance was measured via three-fold repeated cross-validation. A mean area under the receiver operating characteristic curve of 0.83 (95% CI: 0.67-0.99), 0.8 (95% CI: 0.62-0.99), and 0.81 (95% CI: 0.65-0.96) was reached in the three repetitions. High-confidence DL-based predicted subtypes showed significant associations (p < 0.001) with morphological features, i.e. tumor type, histological subtypes, and infiltration type. Furthermore, a significant association was found with programmed cell death ligand 1 (PD-L1) combined positive score (p < 0.001) and FGFR3 mutational status (p = 0.002), with high-confidence basal predictions containing a higher proportion of PD-L1 positive samples and high-confidence luminal predictions a higher proportion of FGFR3-mutated samples. Testing of the DL model on an independent cohort highlighted the importance to accommodate histological subtypes. Taken together, our DL workflow can predict protein-based UTUC subtypes, associated with the presence of targetable alterations, directly from H&E slides., (© 2024 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers., Competing Interests: Declaration of Competing Interest The Authors declare no competing financial or non-financial interests. For transparency, we provide the following information: JNK declares consulting services for Owkin, France, DoMore Diagnostics, Norway, Panakeia, UK, Scailyte, Switzerland, Cancilico, Germany, Mindpeak, Germany, and Histofy, UK; furthermore he holds shares in StratifAI GmbH, Germany, and has received honoraria for lectures by AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, Pfizer and Fresenius. DT holds shares in StraifAI GmbH, Germany and received honoraria for lectures by Bayer. PQ and NW declare research funding from Roche and PQ consulting and speaker services for Roche. MST has recently received honoraria for advisory work in relation to the following companies: Incyte, MindPeak, MSD, BMS and Sonrai; these are all unrelated to this work. No other potential conflicts of interest are reported by any of the authors. The authors received advice from NVIDIA when performing this study, but NVIDIA did not have any role in study design, conducting the experiments, interpretation of the results or decision to submit for publication., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: Tumor cells use adhesion molecules like CD15 or sialylCD15 (sCD15) for metastatic spreading. We analyzed the expression of CD15 and sCD15 in clear cell renal cell carcinoma (ccRCC) regarding prognosis., Methods: A tissue microarray containing tissue specimens of 763 patients with ccRCC was immunohistochemically stained for CD15 and sCD15, their expression quantified using digital image analysis, and the impact on patients' survival analyzed. The cell lines 769p and 786o were stimulated with CD15 or control antibody in vitro and the effects on pathways activating AP-1 and tumor cell migration were examined., Results: ccRCC showed a broad range of CD15 and sCD15 expression. A high CD15 expression was significantly associated with favorable outcome (p &lt; 0.01) and low-grade tumor differentiation (p &lt; 0.001), whereas sCD15 had no significant prognostic value. Tumors with synchronous distant metastasis had a significantly lower CD15 expression compared to tumors without any (p &lt; 0.001) or with metachronous metastasis (p &lt; 0.01). Tumor cell migration was significantly reduced after CD15 stimulation in vitro, but there were no major effects on the activating pathways of AP-1., Conclusion: CD15, but not sCD15, qualifies as a biomarker for risk stratification and as an interesting novel target in ccRCC. Moreover, the data indicate a contribution of CD15 to metachronous metastasis. Further research is warranted to decipher the intracellular pathways of CD15 signaling in ccRCC in order to characterize the CD15 effects on ccRCC more precisely., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Background: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine., Methods: In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables., Findings: We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors., Interpretation: Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work., Funding: The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU., Competing Interests: Declaration of interests JNK has received consulting fees from Owkin; DoMore Diagnostics; Panakeia; and Histofy; furthermore, JNK holds shares and holds a leadership role in StratifAI and has received honoraria for lectures by Bayer, Eisai, Merck Sharp & Dohme (MSD), Bristol-Myers Squibb (BMS), Roche, Pfizer, and Fresenius and has participated on a Data Safety Monitoring Board or Advisory Board for Bayer, Eisai, MSD, BMS, Roche, and Pfizer. PQ and NPW declare research funding from Roche and PQ consulting and speaker services for Roche. JJ declares consulting fees from WHO (Development of Digital Health Solution) and CARE International, Papua New Guinea (Development of Survey Instruments). SF declares grants and contracts from Bundesministerium für Bildung und Forschung, Deutsche Forschungsgemeinschaft, and German Cancer Aid, and payment or honoraria from BMS, MSD, European Society for Medical Oncology, and Deutsche Gesellschaft für Pathologie. DT holds shares in StratifAI. WM is a shareholder of Gemeinschaftspraxis Pathologie Starnberg, a private pathology practice in Germany. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The tumor immune microenvironment (TIME) plays a crucial prognostic and predictive role in solid malignancies such as colorectal cancer (CRC). Nevertheless, scoring systems based on TIME such as the Immunoscore (IS) are rarely used in clinical practice. Among other reasons, this might be due to the additional time required for manual quantification of tumor-associated immune cells or costs associated with proprietary/commercial solutions. To address these issues, we developed a multistain deep learning model (MSDLM) and trained, validated, and tested it on immunohistochemical image data of different immune cell subtypes from over 1000 patients with CRC. Our model showed high prognostic accuracy and outperformed other clinical, molecular, and immune cell-based parameters. It might also be used for therapy response prediction in rectal cancer patients undergoing neoadjuvant therapy. Leveraging artificial intelligence interpretability/explainability methods, we ascertained that the MSDLM's predictions align with recognized antitumor immune response patterns. Consequently, the AImmunoscore (AIS) could emerge as a potential TIME-based decision-making tool for clinicians., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Complementary to synaptophysin and chromogranin A, insulinoma-associated protein 1 (INSM1) has emerged as a sensitive marker for the diagnosis of neuroendocrine neoplasms. Since there are no comparative data regarding INSM1 expression in conventional colorectal adenocarcinomas (CRCs) and colorectal mixed adenoneuroendocrine carcinomas/neuroendocrine carcinomas (MANECs/NECs), we examined INSM1 in a large cohort of conventional CRCs and MANECs/NECs. In conventional CRC, we put a special focus on conventional CRC with diffuse expression of synaptophysin, which carry the risk of being misinterpreted as a MANEC or a NEC. We investigated INSM1 according to the immunoreactive score in our main cohort of 1,033 conventional CRCs and 21 MANECs/NECs in comparison to the expression of synaptophysin and chromogranin A and correlated the results with clinicopathological parameters and patient survival. All MANECs/NECs expressed INSM1, usually showing high or moderate expression (57% high, 34% moderate, and 9% low), which distinguished them from conventional CRCs, which were usually INSM1 negative or low, even if they diffusely expressed synaptophysin. High expression of INSM1 was not observed in conventional CRCs. Chromogranin A was negative/low in most conventional CRCs (99%), but also in most MANECs/NECs (66%). Comparable results were observed in our independent validation cohorts of conventional CRC (n = 274) and MANEC/NEC (n = 19). Similar to synaptophysin, INSM1 expression had no prognostic relevance in conventional CRCs, while true MANEC/NEC showed a highly impaired survival in univariate and multivariate analyses (e.g. disease-specific survival: p < 0.001). MANECs/NECs are a highly aggressive variant of colorectal cancer, which must be reliably identified. High expression of INSM1 distinguishes MANEC/NEC from conventional CRCs with diffuse expression of the standard neuroendocrine marker synaptophysin, which do not share the same dismal prognosis. Therefore, high INSM1 expression is a highly specific/sensitive marker that is supportive for the diagnosis of true colorectal MANEC/NEC., (© 2023 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem., Competing Interests: Declaration of interests J.N.K. reports consulting services for Owkin, France, Panakeia, UK, and DoMore Diagnostics, Norway and has received honoraria for lectures by M.S.D., Eisai, and Fresenius. N.W. has received fees for advisory board activities with BMS, Astellas, GSK, and Amgen, not related to this study. N.W. has received fees for advisory board activities with BMS, Astellas, and Amgen, not related to this study. P.Q. has received fees for advisory board activities with Roche and AMGEN and research funding from Roche through an Innovate UK National Pathology Imaging Consortium grant. H.I.G. has received fees for advisory board activities by AstraZeneca and BMS, not related to this study. M.S.T. is a scientific advisor to Mindpeak and Sonrai Analytics, and has received honoraria recently from BMS, MSD, Roche, Sanofi, and Incyte. He has received grant support from Phillips, Roche, MSD, and Akoya. None of these disclosures are related to this work. D.N.C. has participated in advisory boards for MSD and has received research funding on behalf of the TransSCOT consortium from HalioDx for analyses independent of this study. V.H.K. has served as an invited speaker on behalf of Indica Labs and has received project-based research funding from The Image Analysis Group and Roche outside of the submitted work. No other potential disclosures are reported by any of the authors., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals., Methods: In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology., Results: First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology., Conclusion: Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future., (© 2023. The Author(s).)

Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen, and Stable Diffusion. However, their use in medicine, where imaging data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy-preserving artificial intelligence and can also be used to augment small datasets. We show that diffusion probabilistic models can synthesize high-quality medical data for magnetic resonance imaging (MRI) and computed tomography (CT). For quantitative evaluation, two radiologists rated the quality of the synthesized images regarding "realistic image appearance", "anatomical correctness", and "consistency between slices". Furthermore, we demonstrate that synthetic images can be used in self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (Dice scores, 0.91 [without synthetic data], 0.95 [with synthetic data])., (© 2023. The Author(s).)

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient., Competing Interests: Declaration of interests For transparency, we provide the following information: J.N.K. declares consulting services for Owkin, France; Panakeia, UK; and DoMore Diagnostics, Norway. P.Q. and N.P.W. declare research funding from Roche and PQ consulting and speaker services for Roche. P.Q. is a National Institute of Health Research senior investigator., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL)., Methods: Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer., Results: On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance., Conclusions: Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability., (© 2022. The Author(s).)

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters. It could also be used to predict the response to neoadjuvant therapy in patients with rectal cancer. Using an explainable AI approach, we confirmed that the MSDLM's decisions were based on established cellular patterns of anti-tumor immunity. Hence, the AIS could provide clinicians with a valuable decision-making tool based on the tumor immune microenvironment., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Objective: Bleeding ulcers and erosions are hallmarks of active ulcerative colitis (UC). However, the mechanisms controlling bleeding and mucosal haemostasis remain elusive., Design: We used high-resolution endoscopy and colon tissue samples of active UC (n = 36) as well as experimental models of physical and chemical mucosal damage in mice deficient for peptidyl-arginine deiminase-4 (PAD4), gnotobiotic mice and controls. We employed endoscopy, histochemistry, live-cell microscopy and flow cytometry to study eroded mucosal surfaces during mucosal haemostasis., Results: Erosions and ulcerations in UC were covered by fresh blood, haematin or fibrin visible by endoscopy. Fibrin layers rather than fresh blood or haematin on erosions were inversely correlated with rectal bleeding in UC. Fibrin layers contained ample amounts of neutrophils coaggregated with neutrophil extracellular traps (NETs) with detectable activity of PAD. Transcriptome analyses showed significantly elevated PAD4 expression in active UC. In experimentally inflicted wounds, we found that neutrophils underwent NET formation in a PAD4-dependent manner hours after formation of primary blood clots, and remodelled clots to immunothrombi containing citrullinated histones, even in the absence of microbiota. PAD4-deficient mice experienced an exacerbated course of dextrane sodium sulfate-induced colitis with markedly increased rectal bleeding (96 % vs 10 %) as compared with controls. PAD4-deficient mice failed to remodel blood clots on mucosal wounds eliciting impaired healing. Thus, NET-associated immunothrombi are protective in acute colitis, while insufficient immunothrombosis is associated with rectal bleeding., Conclusion: Our findings uncover that neutrophils induce secondary immunothrombosis by PAD4-dependent mechanisms. Insufficient immunothrombosis may favour rectal bleeding in UC., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Pathological TNM staging (pTNM) is the strongest prognosticator in colorectal carcinoma (CRC) and the foundation of its post-operative clinical management. Tumours that invade pericolic/perirectal adipose tissue generally fall into the pT3 category without further subdivision., Methods: The histological depth of invasion into the pericolic/perirectal fat was digitally and conventionally measured in a training cohort of 950 CRCs (Munich). We biostatistically calculated the optimal cut-off to stratify pT3 CRCs into novel pT3a (≤3 mm)/pT3b (>3 mm) subgroups, which were then validated in two independent cohorts (447 CRCs, Bayreuth/542 CRCs, Mainz)., Results: Compared to pT3a tumours, pT3b CRCs showed significantly worse disease-specific survival, including in pN0 vs pN+ and colonic vs. rectal cancers (DSS: P < 0.001, respectively, pooled analysis of all cohorts). Furthermore, the pT3a/pT3b subclassification remained an independent predictor of survival in multivariate analyses (e.g. DSS: P < 0.001, hazard ratio: 4.41 for pT3b, pooled analysis of all cohorts). While pT2/pT3a CRCs showed similar survival characteristics, pT3b cancers remained a distinct subgroup with dismal survival., Discussion: The delineation of pT3a/pT3b subcategories of CRC based on the histological depth of adipose tissue invasion adds valuable prognostic information to the current pT3 classification and implementation into current staging practices of CRC should be considered., (© 2022. The Author(s).)

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53, and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

Aims: Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5 years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into., Methods and Results: To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing subfields of computational pathology with a focus upon solid tumours. The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in subgroups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high throughout subgroups., Conclusions: Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future., (© 2022 The Authors. Histopathology published by John Wiley & Sons Ltd.)

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer., (© 2022. The Author(s).)

Background and Aims: The initiation of cellular senescence in response to protumorigenic stimuli counteracts malignant progression in (pre)malignant cells. Besides arresting proliferation, cells entering this terminal differentiation state adopt a characteristic senescence-associated secretory phenotype (SASP) which initiates alterations to their microenvironment and effects immunosurveillance of tumorous lesions. However, some effects mediated by senescent cells contribute to disease progression. Currently, the exploration of senescent cells' impact on the tumor microenvironment and the evaluation of senescence as possible target in colorectal cancer (CRC) therapy demand reliable detection of cellular senescence in vivo . Therefore, specific immunohistochemical biomarkers are required. Our aim is to analyze the clinical implications of senescence detection in colorectal carcinoma and to investigate the interactions of senescent tumor cells and their immune microenvironment in vitro and in vivo ., Methods: Senescence was induced in CRC cell lines by low-dose-etoposide treatment and confirmed by Senescence-associated β-galactosidase (SA-β-GAL) staining and fluorescence activated cell sorting (FACS) analysis. Co-cultures of senescent cells and immune cells were established. Multiple cell viability assays, electron microscopy and live cell imaging were conducted. Immunohistochemical (IHC) markers of senescence and immune cell subtypes were studied in a cohort of CRC patients by analyzing a tissue micro array (TMA) and performing digital image analysis. Results were compared to disease-specific survival (DSS) and progression-free survival (PFS)., Results: Varying expression of senescence markers in tumor cells was associated with in- or decreased survival of CRC patients. Proximity analysis of p21-positive senescent tumor cells and cytotoxic T cells revealed a significantly better prognosis for patients in which these cell types have the possibility to directly interact. In vitro , NK-92 cells (mimicking natural killer T cells) or TALL-104 cells (mimicking both cytotoxic T cells and natural killer T cells) led to dose-dependent specific cytotoxicity in >75 % of the senescent CRC cells but <20 % of the proliferating control CRC cells. This immune cell-mediated senolysis seems to be facilitated via direct cell-cell contact inducing apoptosis and granule exocytosis., Conclusion: Counteracting tumorigenesis, cellular senescence is of significant relevance in CRC. We show the dual role of senescence bearing both beneficial and malignancy-promoting potential in vivo . Absence as well as exceeding expression of senescence markers are associated with bad prognosis in CRC. The antitumorigenic potential of senescence induction is determined by tumor micromilieu and immune cell-mediated elimination of senescent cells., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Kellers, Fernandez, Konukiewitz, Schindeldecker, Tagscherer, Heintz, Jesinghaus, Roth and Foersch.)

Background: Special AT-rich sequence-binding protein 2 (SATB2) has emerged as an alternative immunohistochemical marker to CDX2 for colorectal differentiation. However, the distribution and prognostic relevance of SATB2 expression in colorectal carcinoma (CRC) have to be further elucidated., Methods: SATB2 expression was analysed in 1039 CRCs and correlated with clinicopathological and morphological factors, CDX2 expression as well as survival parameters within the overall cohort and in clinicopathological subgroups., Results: SATB2 loss was a strong prognosticator in univariate analyses of the overall cohort ( p < 0.001 for all survival comparisons) and in numerous subcohorts including high-risk scenarios (UICC stage III/high tumour budding). SATB2 retained its prognostic relevance in multivariate analyses of these high-risk scenarios (e.g., UICC stage III: DSS: p = 0.007, HR: 1.95), but not in the overall cohort (DSS: p = 0.1, HR: 1.25). SATB2 loss was more frequent than CDX2 loss (22.2% vs. 10.2%, p < 0.001) and of higher prognostic relevance with only moderate overlap between SATB2/CDX2 expression groups., Conclusions: SATB2 loss is able to identify especially aggressive CRCs in high-risk subgroups. While SATB2 is the prognostically superior immunohistochemical parameter compared to CDX2 in univariate analyses, it appears to be the less sensitive marker for colorectal differentiation as it is lost more frequently.

Background: Immunohistochemical loss of CDX2 has been proposed as a biomarker of dismal survival in colorectal carcinoma (CRC), especially in UICC Stage II/III. However, it remains unclear, how CDX2 expression is related to central hematoxylin-eosin (HE)-based morphologic parameters defined by 2019 WHO classification and how its prognostic relevance is compared to these parameters., Methods: We evaluated CDX2 expression in 1003 CRCs and explored its prognostic relevance compared to CRC subtypes, tumour budding and WHO grade in the overall cohort and in specific subgroups., Results: CDX2-low/absent CRCs were enriched in specific morphologic subtypes, right-sided and microsatellite-instable (MSI-H) CRCs (P < 0.001) and showed worse survival characteristics in the overall cohort/UICC Stage II/III (e.g. DFS: P = 0.005) and in microsatellite stable and left-sided CRCs, but not in MSI-H or right-sided CRCs. Compared with CDX2, all HE-based markers showed a significantly better prognostic discrimination in all scenarios. In multivariate analyses including all morphologic parameters, CDX2 was not an independent prognostic factor., Conclusion: CDX2 loss has some prognostic impact in univariate analyses, but its prognostic relevance is considerably lower compared to central HE-based morphologic parameters defined by the WHO classification and vanishes in multivariate analyses incorporating these factors., (© 2021. The Author(s).)

Different immunohistochemical programmed death-ligand 1 (PD-L1) assays and scorings have been reported to yield variable results in triple-negative breast cancer (TNBC). We compared the analytical concordance and reproducibility of four clinically relevant PD-L1 assays assessing immune cell (IC) score, tumor proportion score (TPS), and combined positive score (CPS) in TNBC. Primary TNBC resection specimens (n = 104) were stained for PD-L1 using VENTANA SP142, VENTANA SP263, DAKO 22C3, and DAKO 28-8. PD-L1 expression was scored according to guidelines on virtual whole slide images by four trained readers. The mean PD-L1 positivity at IC-score ≥1% and CPS ≥1 ranged between 53% and 75% with the highest positivity for SP263 and comparable levels for 22C3, 28-8, and SP142. Inter-assay agreement was good between 28-8 and 22C3 across all scores and cut-offs (kappa 0.68-0.74) and for both assays with SP142 at IC-score ≥1% and CPS ≥1 (kappa 0.61-0.67). The agreement between SP263 and all other assays was substantially lower for all scores. Inter-reader agreement for each assay was good to excellent for IC-score ≥1% (kappa 0.73-0.78) and CPS ≥1 (kappa 0.68-0.74), fair to good for CPS ≥10 (kappa 0.52-0.67) and TPS ≥1% (kappa 0.53-0.72). The percentage of overlapping cases in the positive/negative category was >90% between IC-score ≥1% and CPS ≥1 but below when comparing IC-score ≥1% with CPS ≥10. We demonstrate an overall good inter-reader agreement for all PD-L1 assays in TNBC along with assay specific differences in positivity and concordances, which may aid to select the right test strategy in routine diagnostics., Competing Interests: Declaration of competing interest AN has attended Advisory Boards. DCW has received funding from Roche Pharma AG, BMS, and MSD. KSc has attended Advisory Boards and served as a speaker for Roche, MSD, and BMS. KS has received funding from Roche Pharma AG. MK has received remuneration from Springer Press, Biermann Press, Celgene, AstraZeneca, Myriad Genetics and Teva, received consultancy or advisory fees from Myriad Genetics, KVB, DKMS LIFE, BLÄK and TEVA, holds stock in Therawis Diagnostics GmbH and AIM GmbH and received funding from Sphingotec, Deutsche Krebshilfe, DFG, BMBF, the Senator Roesner Foundation and the Dr Pommer-Jung Foundation. DO is an employee of MSD Sharp & Dohme GmbH. WR has received funding from Roche Pharma AG, has attended Advisory Boards and served as a speaker for Roche, MSD, Novartis. WW has attended Advisory Boards, served as speaker for Roche, MSD, BMS, AstraZeneca, Pfizer, Merck, Lilly, Boehringer, Novartis, Takeda, Bayer, Amgen, Astellas, Illumina, Siemens, Agilent and Molecular Health and receives research funding from Roche, MSD, BMS and AstraZeneca. The remaining authors (SF, SK, AH) have no conflict of interest., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients., Objective: In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC., Design Setting and Participants: Two mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing., Outcome Measurements and Statistical Analysis: Outcome measurements included Harrell's concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent., Results: The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM's prediction was an independent prognostic factor outperforming other clinical parameters., Interpretation: Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease., Patient Summary: An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Schulz, Woerl, Jungmann, Glasner, Stenzel, Strobl, Fernandez, Wagner, Haferkamp, Mildenberger, Roth and Foersch.)

Background: Colorectal mixed adenoneuroendocrine carcinomas (MANECs) are clinically highly aggressive neoplasms. MANECs are composed of variable adenocarcinoma components combined with morphologically distinct neuroendocrine carcinoma components, which are confirmed by synaptophysin immunohistochemistry, the gold standard marker of a neuroendocrine differentiation. However, the biological behavior of adenocarcinomas that express synaptophysin but do not show a typical neuroendocrine morphology remains unclear., Methods: We investigated synaptophysin expression in 1002 conventional colorectal adenocarcinomas and correlated the results with clinicopathological characteristics and patient survival and compared the survival characteristics of synaptophysin expression groups to MANECs., Results: Synaptophysin expression in conventional colorectal adenocarcinomas was associated with a shortened disease-free survival ( p = 0.037), but not with overall survival or disease-specific survival (DSS) in univariate analyses and without any survival impact in multivariate analyses. Patients with "true" MANECs, on the other hand, showed a significantly shorter survival than all conventional adenocarcinomas with or without synaptophysin expression in uni- and multivariate analyses (e.g., multivariate DSS: p < 0.001, HR: 5.20)., Conclusions: Our study demonstrates that synaptophysin expression in conventional colorectal adenocarcinomas, in contrast to MANECs, is not associated with a significantly poorer clinical outcome when compared to adenocarcinomas without synaptophysin expression. Furthermore, our data suggest that conventional adenocarcinomas with a diffuse synaptophysin expression should not be classified as MANECs, also strongly arguing that synaptophysin testing should be reserved for carcinomas with an H&E morphology suggestive of a neuroendocrine differentiation.